Our long-term goal is to develop decision aids that will improve breast cancer treatment. The aesthetic outcome of breast cancer treatment is an important factor in breast cancer survivors' quality of life. Aesthetics refers to physical characteristics such as symmetry and proportion. Currently, physicians, patients, or other observers evaluate breast aesthetics in a subjective, qualitative manner. However, such assessments are typically based on vaguely defined rating scales that have low intra- and inter- observer agreement. Their qualitative nature also restricts the analyses that can be performed. Quantitative, objective measures with high reliability are needed to meaningfully relate patient and surgical variables to aesthetic outcomes and to compare the outcomes of different kinds of breast cancer treatments (e.g., reconstruction procedures). Our approach to quantifying breast aesthetics is to make measurements between anatomical landmarks (fiducial points) on clinical photographs. In the proposed study, we will design and evaluate automatic image processing algorithms for computing objective, quantitative, reproducible measures of breast aesthetics. We anticipate that the new system will lead to future studies in three significant clinical areas: (1) outcome analyses of treatment options (e.g., comparison of breast conservation therapy to mastectomy followed by reconstruction), (2) development of new approaches to patient education about treatment options, and (3) investigations of intra-operative imaging to guide surgeons in breast reconstruction. The specific aims of this study are to: 1) Design algorithms to compute objective, quantitative, reproducible measures of breast aesthetics. 2) Evaluate the algorithms for computing objective measures of breast aesthetic properties. 3) Expand the utility of our methods by designing and evaluating image processing algorithms for automatic identification of fiducial points.